SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 176–200 of 1570 papers

TitleStatusHype
AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing GraphsCode0
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution—0
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction—0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations—0
Causal Discovery on Dependent Binary Data—0
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
FedGIG: Graph Inversion from Gradient in Federated Learning—0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges—0
THeGCN: Temporal Heterophilic Graph Convolutional Network—0
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks—0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal RecommendationCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
Modality-Independent Graph Neural Networks with Global Transformers for Multimodal RecommendationCode2
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and TasksCode0
SPGL: Enhancing Session-based Recommendation with Single Positive Graph LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified